Design of new products is becoming an increasingly complex activity because of reliance on high performance features requiring signal processing and feedback control. Many industries today also rely on complex processes to produce a product. For example, the semiconductor industry uses extremely complicated processes to produce products that typically have very narrow tolerances for final product characteristics. This situation presents a challenge for those designing products and control systems, in part because the design process is very computationally intensive. Similar challenges exist in any area where a complex product must be designed and its final characteristics tightly controlled. Current methodologies for design of products and control systems are inadequate in several respects. For example in the area of control of manufacturing processes or control of product behavior, standard control methods are currently used. Many modern products and manufacturing processes, however, are too complex for standard control methods to satisfactorily control them. Typical prior design methods are linear plans that do not provide alternatives required by the uncertainty of outcomes of computations and tests, and then permit planning based on resource utilization. Also, these prior methods do not incorporate actual experienced results of process execution and adjust projections accordingly.
It is difficult, if not impossible, to achieve satisfactory results using prior methods. There are many problems in applying such methods to complicated manufacturing processes or control of behavior of high performance products.
Prior methods implemented in design tools are not effective for several reasons. For example, prior design tools typically automate linear fragments of the design activity. Results of design steps are thus unknown or uncertain before the steps are actually carried out. For instance compute time, computational errors and exceptions, and results of physical tests cannot be known in advance to aid in decision making. These prior tools require a user to make a large number of complex decisions that depend on results of many previous steps. This is a disadvantage because the user must usually possess specific knowledge or skills in order to properly use the information gained from execution of previous steps. It is a further disadvantage because intelligent decisions can only be made and incorporated after waiting for execution of design steps. No problem-specific guidance is available from prior tools for projecting with any accuracy what the results of design steps will be.
Prior design steps can become infeasible or highly suboptimal because of a user decision made many steps back. Prior design tools cannot help the user see future implications of current decisions. For these reasons, with prior tools, the user must learn by problem-specific experience, over a long period of time, to resolve unknowns and dependencies across design steps.
The problem of adequate control of complex processes is further exacerbated by a current division and separation of skill sets among those involved in the design process. For instance, control experts often do not have an in-depth knowledge of the process they are seeking to control. In addition, the proprietary nature of the processes often does not allow for acquisition of an in-depth knowledge of the process. On the other hand, process experts may know conventional control methods but do not know advanced control methods. Existing control design tools are designed for control experts, but are not suitable for process experts.
Experienced control scientists have found ways to sidestep or solve these problems in specific cases. Significant shortcomings still surface, however, when less experienced control designers or team members from other disciplines apply existing software tools to manufacturing problems. Consequently, current tools are inadequate for widespread use.
Most existing software design tools simply automate fragments of standard design methods. In general, the tools are ineffective when applied to control of manufacturing processes for the reasons described above. FIG. 1 illustrates a conventional process design paradigm. FIG. 1 is a specific example of design of a controller for a thermally activated process. In the conventional paradigm of FIG. 1, scientists 102, process experts 108 and control experts 114 work within different domains with different tools. Scientists 102 typically operate in the domain of thermal models 104 using tools such as Fortran TWOPNT 106 (Grcar, J., The TWOPNT Program for Boundary Value Problems, Sandia National Laboratories, SAND 91-8230, April, 1992). Process experts 108 typically deal with the domain of process monitoring 110 using a tool such as Lab View.RTM. 112 (available from National Instruments, Austin, Tex.). Control experts 114 are typically concerned with the domain of temperature control 116 and use tools such as MATRIX.sub.X.RTM. 118 (available from Integrated Systems, Inc., Sunnyvale, Calif.), or MATLAB.RTM. 118 (available from Mathworks, Inc., Natick, Mass.). Efficient control requires an integration of information from each of domains 104, 110 and 116 in an easily usable format, which typically does not occur in current design tools.